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-driven, machine learning approaches. The biomass data product will be validated by data from an international network of ground-truth forest sites (GEO-TREES, geo-trees.org). The developed algorithms thus
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to develop complement/augment classical CFD methods with quantum algorithms/techniques. The work lies at the intersection of multiphase flow physics, numerical modeling, and quantum computing. Who we
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numerical models to improve the simulation of complex multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid
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on the following areas: Development algorithms and their software implementation in Python and PyTorch Validation of results and comparative analysis of proposed method with baseline approaches Qualifications You
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. The role involves contributing to this research project with a focus on model development, implementation, and testing. Further tasks involve dataset curation, analyzing results, and the creation
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involves evaluating the economic benefit (Value of Information) of these new inventory methods compared to traditional approaches. Duties and Responsibilities: Algorithm Development: Develop and validate
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, experience working with the PyTorch framework, documented ability to develop algorithms and implement them in efficient code, and experience in statistical modeling, optimization or numerical methods, as
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research within Unmanned Traffic Management (UTM). In this role, your primary responsibility will be the hands-on development of advanced simulations and prototypes that help us test and validate new UTM
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algorithms are agnostic of the downstream task they will be deployed on, and this may lead to a suboptimal control performance. In this project, we will investigate control-oriented biases and their impact on
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multiphase phenomena. The study will combine theory, algorithm development, and computational modeling, with the goal of advancing scalable hybrid approaches for next-generation fluid simulations. Who we